Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation

2025-03-26

AI Summary

To address resampling artifacts and information loss arising from highly heterogeneous voxel sizes in multicenter MRI, this work proposes a resampling-free framework for multiple sclerosis lesion segmentation. The core innovation lies in a physically grounded, radius-fixed spherical harmonic parameterization of convolutional kernels, integrated with E(3)-equivariant convolutions and spherical harmonic expansions—enabling voxel-size-invariant and resolution-adaptive feature learning. By preserving the original spatial geometry and physical constraints, the method directly models local structural patterns under variable voxel resolutions. Evaluated on multiple publicly available and internal multicenter MS datasets exhibiting high inter-scanner heterogeneity, our approach significantly outperforms both resampling-based and resampling-free U-Net baselines. It achieves consistent improvements across 2D and most 3D evaluation metrics and demonstrates superior generalizability.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Non-convex Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In the setting of clinical imaging, differences in between vendors, hospitals and sequences can yield highly inhomogeneous imaging data. In MRI in particular, voxel dimension, slice spacing and acquisition plane can vary substantially. For clinical applications, therefore, algorithms must be trained to handle data with various voxel resolutions. The usual strategy to deal with heterogeneity of resolution is harmonization: resampling imaging data to a common (usually isovoxel) resolution. This can lead to loss of fidelity arising from interpolation artifacts out-of-plane and downsampling in-plane. We present in this paper a network architecture designed to be able to learn directly from spatially heterogeneous data, without resampling: a segmentation network based on the e3nn framework that leverages a spherical harmonic, rather than voxel-grid, parameterization of convolutional kernels, with a fixed physical radius. Networks based on these kernels can be resampled to their input voxel dimensions. We trained and tested our network on a publicly available dataset assembled from three centres, and on an in-house dataset of Multiple Sclerosis cases with a high degree of spatial inhomogeneity. We compared our approach to a standard U-Net with two strategies for handling inhomogeneous data: training directly on the data without resampling, and resampling to a common resolution of 1mm isovoxels. We show that our network is able to learn from various combinations of voxel sizes and outperforms classical U-Nets on 2D testing cases and most 3D testing cases. This shows an ability to generalize well when tested on image resolutions not seen during training. Our code can be found at: http://github.com/SCAN-NRAD/e3nn_U-Net.

Problem

• Handles spatially inhomogeneous MRI data without resampling
• Improves Multiple Sclerosis lesion segmentation across varying resolutions
• Outperforms standard U-Nets on diverse resolution test cases

Innovation

• Resolution-adaptive convolutions for inhomogeneous data
• Spherical harmonic parameterization of kernels
• Fixed physical radius for kernel resampling